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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (1): 152-159    DOI: 10.3785/j.issn.1008-973X.2020.01.018
计算机技术、信息工程     
改进边界Fisher分析近邻选择的硬件木马检测
王晓晗(),王韬*(),张阳,刘广凯
中国人民解放军陆军工程大学石家庄校区 装备模拟训练中心，河北 石家庄 050003
Hardware Trojan detection based on improved marginal Fisher analysis nearest neighbor selection
Xiao-han WANG(),Tao WANG*(),Yang ZHANG,Guang-kai LIU
Equipment Simulation Training Center, Army Engineering University Shijiazhuang Campus, Shijiazhuang 050003, China
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摘要：

针对旁路分析技术对小规模硬件木马检测精度低的问题，提出基于边界Fisher分析的硬件木马检测方法.定义规则式选择近邻样本，以减小样本与其同类近邻样本间距离和增大样本与其异类近邻样本间距离的方式构建投影子空间，在不对数据分布作任何假设的前提下，提取原始功耗旁路信号中的差异特征，实现硬件木马检测. AES加密电路中的硬件木马检测实验表明，该方法能够检测出占原始电路规模0.02%的硬件木马，优于已有的检测方法.
关键词： 集成电路;  旁路分析;  硬件木马检测;  流形学习;  边界Fisher分析    
Abstract:

A hardware Trojan detection method based on marginal Fisher analysis was proposed aiming at the problem of low detection accuracy for small-scale hardware Trojan by side-channel analysis. The rule was defined to select the nearest neighbor samples. Then the projection subspace was constructed by reducing the distance between the samples and their nearest neighbor samples in the same class and increasing the distance between the samples and their nearest neighbor samples in different class. The difference features in the original side-channel signals was found without any assumptions about data distribution, and the detection of hardware Trojan was achieved. The hardware Trojan detection experiment in AES encryption circuit shows that the hardware Trojan whose scale is 0.02% of the original circuit can be detected by the method. The method is better than existing detection methods.
Key words: integrated circuit    side-channel analysis    hardware Trojan detection    manifold learning    marginal Fisher analysis
收稿日期: 2019-03-06 出版日期: 2020-01-05
CLC:  TN 918  
基金资助: 国家自然科学基金资助项目（61602505）
通讯作者: 王韬     E-mail: wxh2225@126.com;T_Wang_mail@163.com
作者简介: 王晓晗（1992—），男，博士生，从事芯片安全技术的研究. orcid.org/0000-0001-8878-1443. E-mail： wxh2225@126.com
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引用本文:

王晓晗,王韬,张阳,刘广凯. 改进边界Fisher分析近邻选择的硬件木马检测[J]. 浙江大学学报(工学版), 2020, 54(1): 152-159.

Xiao-han WANG,Tao WANG,Yang ZHANG,Guang-kai LIU. Hardware Trojan detection based on improved marginal Fisher analysis nearest neighbor selection. Journal of ZheJiang University (Engineering Science), 2020, 54(1): 152-159.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.01.018    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I1/152

图 1  MFA方法分析
图 2  改进后样本的运动趋势
图 3  采用K-L变换、核MMC、MFA和改进MFA对木马1的检测结果
图 4  采用K-L变换、核MMC、MFA和改进MFA对木马2的检测结果
图 5  不同规模硬件木马的检测结果对比
图 6  10个待测芯片的检测结果
图 7  采用改进MFA进行第1组实验的检测结果
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